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  <div class="section" id="numpy-nanprod">
<h1>numpy.nanprod<a class="headerlink" href="#numpy-nanprod" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.nanprod">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">nanprod</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">keepdims=&lt;no value&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/lib/nanfunctions.py#L657-L720"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.nanprod" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the product of array elements over a given axis treating Not a
Numbers (NaNs) as ones.</p>
<p>One is returned for slices that are all-NaN or empty.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.10.0.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Array containing numbers whose product is desired. If <em class="xref py py-obj">a</em> is not an
array, a conversion is attempted.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">{int, tuple of int, None}, optional</span></dt><dd><p>Axis or axes along which the product is computed. The default is to compute
the product of the flattened array.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">data-type, optional</span></dt><dd><p>The type of the returned array and of the accumulator in which the
elements are summed.  By default, the dtype of <em class="xref py py-obj">a</em> is used.  An
exception is when <em class="xref py py-obj">a</em> has an integer type with less precision than
the platform (u)intp. In that case, the default will be either
(u)int32 or (u)int64 depending on whether the platform is 32 or 64
bits. For inexact inputs, dtype must be inexact.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternate output array in which to place the result.  The default
is <code class="docutils literal notranslate"><span class="pre">None</span></code>. If provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
<em class="xref py py-obj">ufuncs-output-type</em> for more details. The casting of NaN to integer
can yield unexpected results.</p>
</dd>
<dt><strong>keepdims</strong><span class="classifier">bool, optional</span></dt><dd><p>If True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will
broadcast correctly against the original <em class="xref py py-obj">arr</em>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>nanprod</strong><span class="classifier">ndarray</span></dt><dd><p>A new array holding the result is returned unless <em class="xref py py-obj">out</em> is
specified, in which case it is returned.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.prod.html#numpy.prod" title="numpy.prod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.prod</span></code></a></dt><dd><p>Product across array propagating NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.isnan.html#numpy.isnan" title="numpy.isnan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isnan</span></code></a></dt><dd><p>Show which elements are NaN.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="go">1</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">([</span><span class="mi">1</span><span class="p">])</span>
<span class="go">1</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">])</span>
<span class="go">1.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">6.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([3., 2.])</span>
</pre></div>
</div>
</dd></dl>

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